Training material for all kinds of transcriptomics analysis.
Before diving into this topic, we recommend you to have a look at:
- Introduction to Galaxy Analyses
- Sequence analysis
You can use a public Galaxy instance which has been tested for the availability of the used tools. They are listed along with the tutorials above.
You can also use the following Docker image for these tutorials:
docker run -p 8080:80 quay.io/galaxy/transcriptomics-training
NOTE: Use the -d flag at the end of the command if you want to automatically download all the data-libraries into the container.
It will launch a flavored Galaxy instance available on http://localhost:8080. This instance will contain all the tools and workflows to follow the tutorials in this topic. Login as admin with password admin to access everything.
This material is maintained by:Bérénice Batut, Maria Doyle
For any question related to this topic and the content, you can contact them or visit our Gitter channel.
This material was contributed to by:Bérénice Batut, Anika Erxleben, Markus Wolfien, Florian Heyl, Daniel Maticzka, Mallory Freeberg, Mo Heydarian, Mehmet Tekman, Alex Ostrovsky, IGC Bioinformatics Unit, Maria Doyle, Chao (Cico) Zhang, Wendi Bacon, Hans-Rudolf Hotz, Daniel Blankenberg, Wolfgang Maier, Fotis E. Psomopoulos, Toby Hodges, Pavankumar Videm, Belinda Phipson, Harriet Dashnow, Jovana Maksimovic, Anna Trigos, Matt Ritchie, Shian Su, Charity Law, Clemens Blank, Nicola Soranzo, Peter van Heusden, Graham Etherington, Andrea Bagnacani
- Shirley Pepke et al: Computation for ChIP-seq and RNA-seq studies
Paul L. Auer & R. W. Doerge: Statistical Design and Analysis of RNA Sequencing Data
Insights into proper planning of your RNA-seq run! To read before any RNA-seq experiment!
Ian Korf: Genomics: the state of the art in RNA-seq analysis
A refreshingly honest view on the non-trivial aspects of RNA-seq analysis
Marie-Agnès Dillies et al: A comprehensive evaluation of normalization methods for Illumina high-throughput RNA sequencing data analysis
Systematic comparison of seven representative normalization methods for the differential analysis of RNA-seq data (Total Count, Upper Quartile, Median (Med), DESeq, edgeR, Quantile and Reads Per Kilobase per Million mapped reads (RPKM) normalization)
Franck Rapaport et al: Comprehensive evaluation of differential gene expression analysis methods for RNA-seq data
Evaluation of methods for differential gene expression analysis
- Charlotte Soneson & Mauro Delorenzi: A comparison of methods for differential expression analysis of RNA-seq data
- Adam Roberts et al: Improving RNA-Seq expression estimates by correcting for fragment bias
Manuel Garber et al: Computational methods for transcriptome annotation and quantification using RNA-seq
Classical paper about the computational aspects of RNA-seq data analysis
- Cole Trapnell et al: Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks
- Zhong Wang et al: RNA-Seq: a revolutionary tool for transcriptomics
- Dittrich, M. T. and Klau, G. W. and Rosenwald, A. and Dandekar, T. and Muller, T.: Identifying functional modules in protein-protein interaction networks: an integrated exact approach
- May, Ali; Brandt, Bernd W; El-Kebir, Mohammed; Klau, Gunnar W; Zaura, Egija; Crielaard, Wim; Heringa, Jaap; Abeln, Sanne: metaModules identifies key functional subnetworks in microbiome-related disease
- Pavankumar, Videm; Dominic, Rose; Fabrizio, Costa; Rolf, Backofen: BlockClust: efficient clustering and classification of non-coding RNAs from short read RNA-seq profiles